Yeah - the whole business model of companies like OpenAI and Anthropic, at least at the moment, seems to be that the models are so big that you need to run them in the cloud with metered access. Maybe that could change in the future to sale or annual licence business model if running locally became possible.
I think scale helps for general tasks where the breadth of capability may be needed, but it's not so clear that this needed for narrow verticals, especially something like coding (knowing how to fix car engines, or distinguish 100 breeds of dog is not of much use!).
> the whole business model of companies like OpenAI and Anthropic, at least at the moment, seems to be that the models are so big that you need to run them in the cloud with metered access.
That's not a business model choice, though. That's a reality of running SOTA models.
If OpenAI or Anthropic could squeeze the same output out of smaller GPUs and servers they'd be doing it for themselves. It would cut their datacenter spend dramatically.
> If OpenAI or Anthropic could squeeze the same output out of smaller GPUs and servers they'd be doing it for themselves.
First, they do this; that's why they release models at different price points. It's also why GPT-5 tries auto-routing requests to the most cost-effective model.
Second, be careful about considering the incentives of these companies. They all act as if they're in an existential race to deliver 'the' best model; the winner-take-all model justifies their collective trillion dollar-ish valuation. In that race, delivering 97% of the performance at 10% of the cost is a distraction.
No I don’t think it’s a business model thing, I’m saying it may be a technical limitation of LLMs themselves. Like, that that there’s no way to “order a la carte” from the training process, you either get the buffet or nothing, no matter how hungry you feel.
I think scale helps for general tasks where the breadth of capability may be needed, but it's not so clear that this needed for narrow verticals, especially something like coding (knowing how to fix car engines, or distinguish 100 breeds of dog is not of much use!).